AI Product & Infrastructure
Turn model capabilities into products customers actually want
Most AI initiatives stall between proof-of-concept and production. I help you bridge that gap, from finding the use cases worth pursuing, to designing AI-as-a-Service offerings, to building the platform, governance and team practices that make AI a durable part of your product, not a one-off experiment.
The full AI product lifecycle
Each area can be engaged on its own or combined into a broader AI product leadership engagement. The goal is always the same, turning AI capability into measurable customer and business value.
AI Use Case Discovery & Prioritisation
Cut through the AI hype to find the use cases that genuinely move your business metrics, not just the ones that demo well.
- Structured use case canvassing and value scoring
- Feasibility, data readiness and effort assessment
- Build-vs-buy-vs-partner decision frameworks
- Prioritised AI opportunity backlog tied to outcomes
AI Product Strategy & Roadmapping
Define where AI creates durable competitive advantage and build a phased roadmap that delivers value at every step.
- AI product vision and strategic positioning
- Capability roadmap from pilot to platform
- Investment case and ROI modelling for AI initiatives
- Competitive analysis and differentiation strategy
AI-as-a-Service Offering Design
Productise your ML capabilities into customer-facing offerings with clear packaging, pricing and value propositions.
- Offering architecture and packaging design
- Consumption and outcome-based pricing models
- API and integration design for customer adoption
- Self-service and developer experience strategy
ML Platform & MLOps Architecture
Define the platform architecture and tooling strategy that lets your teams experiment rapidly and ship to production safely. I work alongside your engineering teams or specialist ML engineers to implement, rather than replacing them.
- ML platform architecture definition and technology selection
- MLOps tooling strategy: CI/CD, model registries and pipelines
- GPU and inference infrastructure planning and sizing
- Observability and experiment tracking framework design
AI Infrastructure, GPU Compute & Licensing
Work out what the infrastructure under your AI products should cost before you buy it, including which parts of a proprietary stack you actually need to pay for.
- GPU and accelerated compute strategy, bare metal and cloud-native provisioning
- Reference architectures and consumption models for AI infrastructure
- Licensing analysis, including open source alternatives to proprietary AI stacks
- Managed services design so customers can actually run what they buy
Cloud & Platform Architecture
Private, public and hybrid platforms that are standardised, costed and resilient, so teams build products rather than infrastructure.
- Private and hybrid cloud reference architecture on VMware and AWS
- Standardised sizing and pricing models that sales can use directly
- Resilience and disaster recovery design
- Cost optimisation tied to business value, not just spend
From concept to production AI
A pragmatic, evidence-led approach that de-risks AI investment at every stage, so you build what customers need and can sustain it in production.
Explore
Identify high-value AI use cases aligned to business strategy, assess data readiness, and define success metrics before committing investment.
Validate
Rapidly prototype and test AI concepts with real customers, validating technical feasibility, market demand and business value.
Build
Design ML platform architecture, implement AI features with MLOps practices, and establish the foundations for sustainable, repeatable deployment.
Iterate
Monitor model performance and business impact, gather customer feedback, and continuously improve AI capabilities based on real-world usage.
Deepening the practice
To keep this offering honest and current, I am working through a structured set of Microsoft AI and cloud certifications. These are in progress or planned, not yet earned, and will extend the product, platform and governance capabilities above as they complete.
AI Business Professional
Using generative AI and Microsoft 365 Copilot to enhance business workflows and outcomes, no coding required. Grounds my use case discovery and AI-as-a-service work in real-world productivity tooling.
AI Transformation Leader
Executive-level AI strategy, launch and scale across Microsoft 365 Copilot, Azure AI, Microsoft Foundry and Power BI. Strengthens the AI product strategy and transformation side of the practice.
AI Agent Builder Associate
Designing and building integrated AI agent solutions in Copilot Studio. Adds hands-on agentic AI build depth to the platform architecture work.
Intelligent Applications Builder Associate
Building AI-powered business applications across Power Platform, Dataverse, Power Automate, Copilot Studio and AI Builder. Adds low-code app delivery to the platform architecture work.
Agentic AI Business Solutions Architect
Architecting scalable, secure, integrated agentic AI solutions across the Microsoft AI platform. The expert-level capstone that ties strategy, platform and governance together.
Azure Data Fundamentals
Core data concepts and Azure data services. Underpins the data readiness and AI infrastructure conversations.
Identity & Access Administrator
Designing and operating identity and access management. Brings security and access control depth to responsible AI and platform governance.
Capabilities in progress
Each of these unlocks as a specific course or prototype finishes, so this offering only grows as far as the work behind it actually goes.
Low-Code AI App Delivery
Building AI-powered business applications on Power Platform, Dataverse and AI Builder, for teams who need a working app faster than a full custom build.
Agentic AI Solution Design
Architecting multi-agent solutions across Microsoft 365 Copilot, Azure AI and Microsoft Foundry, from business case through to a working design.
Copilot Studio Agent Builds
Hands-on build of integrated AI agents in Copilot Studio, not just the design behind them.
AI Training Platform Blueprints
A repeatable blueprint for standing up an AI training or enablement platform, drawn from building Cloudio Learning solo, including what the internationalisation retrofit taught me.
AI-Builder Codebase Assessment
Structured architecture review of AI-builder-generated codebases (Base44, Replit and similar), surfacing what a vibe-coded app actually contains before you rely on it.
AI Identity & Access Governance
Designing access control and governance for AI agents and the systems they touch, not just for people.
AI Data Platform Advisory
Assessing whether your data platform is actually ready to support AI workloads, and what to fix first if it is not.
Common questions
A few things people often ask before starting an AI product engagement.
Do you build AI models and algorithms?
I focus on product strategy, platform architecture and productisation rather than core ML engineering. I work alongside your data science teams or recommend specialist ML engineers as needed, making sure the product and platform decisions around the model are sound.
Which AI platforms and tools do you work with?
My hands-on depth is in NVIDIA AI Enterprise and the open source stack around it, including Slurm, Kubernetes, Triton and MLflow, plus private cloud on VMware and public cloud on AWS and Azure. I recommend what fits your needs, your team's skills and the infrastructure you already have, not what I happen to know best.
How do you approach AI ethics and responsible AI?
I integrate ethical considerations into product design from the start, helping you build practical frameworks for bias detection, transparency and responsible AI deployment that fit your regulatory context and customer expectations.
What if we do not have the data needed for AI?
Data strategy is foundational for AI success. I help you assess data readiness, identify gaps and develop pragmatic strategies to collect, buy or prepare the data you need, so AI investment is not wasted on weak foundations.
Where I have done this before
Real work behind this service, written up in full on the portfolio.
AI-as-a-Service
A component-level open source alternative to a proprietary AI stack, and the managed services gap it exposed.
Read the case studyBare Metal as a Service
Revived a failed product by integrating Canonical MaaS rather than rebuilding it.
Read the case studyOnDemand CX
A private cloud reference architecture and self-serve pricing tool that opened an enterprise market.
Read the case studyStart with discovery
Which AI use cases are actually worth it?
Before investing in models or infrastructure, a discovery session maps your AI opportunities, scores them for value and feasibility, and gives you a prioritised shortlist backed by evidence, not hype.
A scoping call is free. A structured AI discovery with a scored opportunity matrix and phased roadmap is a fixed-price piece of work you own.